(iii) Inspired by the auxiliary task approach towards improving primary task objective [1]}, we aim to decode the ground truth character sequence \(\hat{Y}\)  directly from \(z\)  by using an auxiliary character sequence decoder. Firstly, this approach tackles the problem of vanishing latent variable [2]} and provides better gradient to regularize the learning of feedback module. Secondly, we discover a choice of training our feedback module (refer Figure REF ) via relativistic information of prediction:ground-truth pairs (say \(\mathcal {G}\) ) generated from other state-of-the-arts, instead of depending on \(\mathbf {T}\)  predicted \(Y\)  alone. This helps the module in learning an associative relation between candidate correct words and closely related erroneous instances. Loosely speaking `\(hello\) ' might be predicted as `\(nello\) ' or `\(bello\) ' due to a partial structural similarity of `\(h\) ' with `\(n\) ' or `\(b\) '. Learning this sense from error distribution imparts the model a semantic knowledge of required character association to form a valid word. Additionally, using text-only data \(\mathcal {G}\)  alleviates the issue of limited availability of image-paired datasets during training.
